Results 1  10
of
3,681,278
Efficient Adaptive Collect using Randomization
 PROC. OF THE INTL. SYMP. ON DISTRIBUTED COMPUTING (DISC
, 2004
"... An adaptive algorithm, whose step complexity adjusts to the number of active processes, is attractive for distributed systems with a highlyvariable number of processes. The cornerstone of many adaptive algorithms is an adaptive mechanism to collect uptodate information from all participating p ..."
Abstract

Cited by 24 (2 self)
 Add to MetaCart
An adaptive algorithm, whose step complexity adjusts to the number of active processes, is attractive for distributed systems with a highlyvariable number of processes. The cornerstone of many adaptive algorithms is an adaptive mechanism to collect uptodate information from all participating
Efficient Adaptive Collect using Randomization \Lambda
"... Abstract An adaptive algorithm, whose step complexity adjusts to the number of active processes, is attractivefor distributed systems with a highlyvariable number of processes. The cornerstone of many adaptive algorithms is an adaptive mechanism to collect uptodate information from all participat ..."
Abstract
 Add to MetaCart
Abstract An adaptive algorithm, whose step complexity adjusts to the number of active processes, is attractivefor distributed systems with a highlyvariable number of processes. The cornerstone of many adaptive algorithms is an adaptive mechanism to collect uptodate information from all
Randomized Algorithms
, 1995
"... Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available, or the simp ..."
Abstract

Cited by 2210 (37 self)
 Add to MetaCart
, or the simplest, or both. A randomized algorithm is an algorithm that uses random numbers to influence the choices it makes in the course of its computation. Thus its behavior (typically quantified as running time or quality of output) varies from
GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES
, 2002
"... GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phas ..."
Abstract

Cited by 637 (79 self)
 Add to MetaCart
based intensification and postoptimization techniques using pathrelinking. Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.
Random Oracles are Practical: A Paradigm for Designing Efficient Protocols
, 1995
"... We argue that the random oracle model  where all parties have access to a public random oracle  provides a bridge between cryptographic theory and cryptographic practice. In the paradigm we suggest, a practical protocol P is produced by first devising and proving correct a protocol P R for the ..."
Abstract

Cited by 1643 (75 self)
 Add to MetaCart
for the random oracle model, and then replacing oracle accesses by the computation of an "appropriately chosen" function h. This paradigm yields protocols much more efficient than standard ones while retaining many of the advantages of provable security. We illustrate these gains for problems including
An Adaptive EnergyEfficient MAC Protocol for Wireless Sensor Networks
 SENSYS'03
, 2003
"... In this paper we describe TMAC, a contentionbased Medium Access Control protocol for wireless sensor networks. Applications for these networks have some characteristics (low message rate, insensitivity to latency) that can be exploited to reduce energy consumption by introducing an active/sleep du ..."
Abstract

Cited by 526 (13 self)
 Add to MetaCart
/sleep duty cycle. To handle load variations in time and location TMAC introduces an adaptive duty cycle in a novel way: by dynamically ending the active part of it. This reduces the amount of energy wasted on idle listening, in which nodes wait for potentially incoming messages, while still maintaining a
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
Abstract

Cited by 707 (18 self)
 Add to MetaCart
boosting algorithm for combining preferences called RankBoost. We also describe an efficient implementation of the algorithm for certain natural cases. We discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different WWW
Inducing Features of Random Fields
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
Abstract

Cited by 664 (14 self)
 Add to MetaCart
the KullbackLeibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random field models and techniques
Implementing data cubes efficiently
 In SIGMOD
, 1996
"... Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total ..."
Abstract

Cited by 545 (1 self)
 Add to MetaCart
to materializing the data cube. In this paper, we investigate the issue of which cells (views) to materialize when it is too expensive to materialize all views. A lattice framework is used to express dependencies among views. We present greedy algorithms that work off this lattice and determine a good set of views
Scatter/Gather: A Clusterbased Approach to Browsing Large Document Collections
, 1992
"... Document clustering has not been well received as an information retrieval tool. Objections to its use fall into two main categories: first, that clustering is too slow for large corpora (with running time often quadratic in the number of documents); and second, that clustering does not appreciably ..."
Abstract

Cited by 772 (12 self)
 Add to MetaCart
Document clustering has not been well received as an information retrieval tool. Objections to its use fall into two main categories: first, that clustering is too slow for large corpora (with running time often quadratic in the number of documents); and second, that clustering does not appreciably
Results 1  10
of
3,681,278